FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization

A new machine-learning architecture called FinInvest-GTCN reframes venture capital selection as a quantitative risk-return problem, reporting a reduction in error and an 18.7% simulated portfolio gain over baselines, according to research posted on arXiv [1]. The model combines three components: a relational graph encoder that maps the investment ecosystem’s topology, a multi-scale temporal fusion module designed for non-stationary time series, and a causal decision head that produces risk-adjusted predictions alongside interpretable causal attributions [1][3]. The authors argue that traditional content-recommendation approaches fail to handle the heterogeneous, low-data conditions typical of venture capital, so they recast the task as a direct investment-analysis objective [1][4]. A central feature is the Meta-Causal Adaptation strategy, which regularizes fine-tuning toward causally plausible structures learned during meta-pretraining. The paper states that this allows the model to adapt to new, data-scarce sectors without sacrificing stability [1][4]. On proprietary VC datasets, FinInvest-GTCN lowered the primary Risk-Adjusted Mean Squared Error to 2.51, compared with a baseline of 3.05 [1][2]. In a simulated portfolio, cumulative return improved by 18.7% [1][3]. Ablation studies confirmed that each architectural component contributed to the gains, and additional tests supported the model’s stability and interpretability [1][2]. Other recent work has also explored causal and graph-based methods for financial decisions. A framework called FinCARE combined statistical causal discovery with financial knowledge graphs and large language models, achieving a 43% improvement in counterfactual predictions over correlation-based methods for portfolio management [6]. Separately, a stock recommender named PfoTGNRec used temporal graph networks and mean-variance efficient sampling to balance user preferences with diversification, reporting a 3.45% lift in a combined metric over dynamic-embedding baselines [5]. These efforts share an emphasis on moving beyond black-box predictions toward explanations that can support human decision-making and regulatory review [6][7]. The FinInvest-GTCN paper does not include external validation on live portfolios, and its experiments rely on proprietary data that the authors have not released publicly [1][3]. The research appears as a preprint on arXiv and has not yet been peer-reviewed [1].